SOTAVerified

Intrusion Detection

Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.

Source: Machine Learning Techniques for Intrusion Detection

Papers

Showing 541550 of 800 papers

TitleStatusHype
Exploring Cybersecurity Issues in 5G Enabled Electric Vehicle Charging Station with Deep Learning0
Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection0
A multiagent based framework secured with layered SVM-based IDS for remote healthcare systems0
Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review0
Performance Evaluation of Machine Learning Techniques for DoS Detection in Wireless Sensor Network0
Exploring Edge TPU for Network Intrusion Detection in IoT0
Evaluating Document Coherence Modelling0
Cyber Intrusion Detection by Using Deep Neural Networks with Attack-sharing Loss0
Efficient Intrusion Detection Using Evidence Theory0
Image Classifiers for Network Intrusions0
Show:102550
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13Unverified
2K-Nearest NeighborsAccuracy (%)98.07Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9Unverified